default student balance income
1 No No 729.5265 44361.625
2 No Yes 817.1804 12106.135
3 No No 1073.5492 31767.139
4 No No 529.2506 35704.494
5 No No 785.6559 38463.496
6 No Yes 919.5885 7491.559
7 No No 825.5133 24905.227
8 No Yes 808.6675 17600.451
9 No No 1161.0579 37468.529
10 No No 0.0000 29275.268
11 No Yes 0.0000 21871.073
12 No Yes 1220.5838 13268.562
13 No No 237.0451 28251.695
14 No No 606.7423 44994.556
15 No No 1112.9684 23810.174Demystifying Data
05 - ML and Models and Data - Oh My!!!
Mick Cooney mcooney@describedata.com
What is Machine Learning?
A Word of Caution
Dan Ariely
Big Data is like teenage sex:
everyone talks about it
nobody really knows how to do it
everyone thinks everyone else is doing it
so everyone claims they are doing it.
Terminology
Different meanings
Machine Learning
Artificial Intelligence
Statistical Learning
Applied Statistics
Historical context important
ML primarily from CS / EE
‘Engineering’ mentality
Data Format
Tabular based
Exchangeability
Predictive Focus
Predictive accuracy
De-emphasises inference / uncertainty / explainability
Discoverability of model parameters
Example of linear models
Production
Scaling issues
Automated ML pipelines
Software engineering
Model Validation
Overfitting
Bias-Variance Tradeoff
\[\begin{eqnarray*} \text{Bias} &=& \text{under-complexity error} \\ \text{Variance} &=& \text{over-complexity error} \end{eqnarray*}\]
Cross-validation
Training-test split
\(k\)-fold
Train-validation-test split
Supervised Learning
Labelled data
\[ \begin{eqnarray*} \text{Discrete output} &\rightarrow& \text{Categorisation} \\ \text{Continuous output} &\rightarrow& \text{Regression} \end{eqnarray*} \]
Linear Models
Assumes data follows distributional form
Linear in parameters
Decision / Regression Trees
default student balance income
1 No No 729.5265 44361.625
2 No Yes 817.1804 12106.135
3 No No 1073.5492 31767.139
4 No No 529.2506 35704.494
5 No No 785.6559 38463.496
6 No Yes 919.5885 7491.559
7 No No 825.5133 24905.227
8 No Yes 808.6675 17600.451
9 No No 1161.0579 37468.529
10 No No 0.0000 29275.268
11 No Yes 0.0000 21871.073
12 No Yes 1220.5838 13268.562
13 No No 237.0451 28251.695
14 No No 606.7423 44994.556
15 No No 1112.9684 23810.174
Simple to understand
Highly explainable
Prone to overfitting
Random Forest
Ensemble of trees
Aggregate low-bias trees to reduce variance
Sample of rows, constrain splits
Self-tuning (mostly)
Boosting
Ensemble of trees
Aggregate low-variance trees to reduce bias
Probably most performant approach
Tuning more involved
Support Vector Machines (SVM)
Geometric method
Divides ‘feature space’ into regions
–
Neural Networks
Unsupervised Learning
Unlabelled data
Clustering
Real-world Example
Topic Modelling
Dimensionality Reduction
Natural Language Processing
More prevalent recently
Supervised / Unsupervised / Semi-supervised
Google Translate
Uses neural networks
Very large models (APIs)
Entity Extraction
IRISHMEN AND IRISHWOMEN: In the name of God and of the dead generations from which she receives her old tradition of nationhood, Ireland, through us, summons her children to her flag and strikes for her freedom.
| doc_id | token | lemma | upos | relation |
|---|---|---|---|---|
| 1 | IRISHMEN | Irishmen | NOUN | root |
| 1 | AND | and | CCONJ | cc |
| 1 | IRISHWOMEN | IRISHWOMEN | NOUN | conj |
| 1 | : | : | PUNCT | punct |
| 1 | In | in | ADP | case |
| 1 | the | the | DET | det |
| 1 | name | name | NOUN | obl |
| 1 | of | of | ADP | case |
| 1 | God | God | PROPN | nmod |
| 1 | and | and | CCONJ | cc |
| 1 | of | of | ADP | case |
| 1 | the | the | DET | det |
| 1 | dead | dead | ADJ | amod |
| 1 | generations | generation | NOUN | conj |
| 1 | from | from | ADP | case |
Latent Dirichlet Allocation (LDA)
Unsupervised (clustering)
Topic modelling
Lots of functionality
Assign “topics” to each “document”
Reference: http://dontloo.github.io/blog/lda/
word2vec
Words as vectors
Semantic meaning
\[ \text{King} - \text{Male} + \text{Female} \approx \text{Queen} \]
\[ \text{Paris} - \text{France} + \text{UK} \approx \text{London} \]
Summary
Thank You
mcooney@describedata.com
https://kaybenleroll.github.io/data_workshops/talk_cirdas_master_202311/